Collaborative Research: Pilot Research on Language-Based Strategies for Creative Problem Solving

协作研究：基于语言的创造性问题解决策略的试点研究

• 批准号: 0757193
• 负责人: Richard Gerrig
• 金额: $ 9.97万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0757193&HistoricalAwards=false
• 关键词: Collaborative; Research; Pilot; Language; Based

When people reformulate a problem space, previously unseen structure emerges. This process can be decomposed into two steps: People must first recognize and then exploit novel structure. We suggest that both of these steps can be improved by experienced application of creative nominalization. Here, nominalization refers to the process of recognizing a novel concept and naming it appropriately. This project demonstrates that experience in nominalization can improve problem solving and that successful training and experience on nominalization has the potential to enhance people?s intrinsic motivation, and thereby effectiveness, with respect to creative aspects of problem solving. In parallel, the project explores the potential for nominalization as a strategy to enhance machine-learning agents in reinforcement learning environments. Inspired by research on animal learning, reinforcement learning is a branch of artificial intelligence research concerned with creating motivated, learning agents. In the reinforcement-learning setting, nominalization has the potential to create a first-class object, something that can be directly manipulated, recorded, analyzed, and composed with other objects to form higher-order structures. In addition, reinforcement-learning researchers have recently begun to consider how learning might be enhanced with intrinsic motivation to explore problem spaces. Thus nominalization can function in reinforcement-learning settings both as a direct strategy and indirectly via intrinsic motivation. The most significant broader impact of this project will be to provide a new intervention that will enhance the creativity and efficacy of problem solvers working alone or in collaborative groups. If successful, the relative simplicity of the intervention and its general applicability would make it a prime candidate for wide dispersal to people in disparate walks of like.

当人们重新定义一个问题空间时，以前看不见的结构就会出现。 这个过程可以分解为两个步骤：首先，人们必须认识并开发新结构。我们认为这两个步骤都可以通过有经验的创造性名词化的应用来改进。 名词化指的是对一个新概念的认识和恰当命名的过程。 这个项目表明，名词化的经验可以提高解决问题的能力，成功的名词化训练和经验有潜力提高人们的能力。的内在动机，从而有效性，相对于创造性方面的问题解决。 同时，该项目探讨了名词化作为强化学习环境中增强机器学习代理的策略的潜力。 受动物学习研究的启发，强化学习是人工智能研究的一个分支，涉及创建有动机的学习代理。 在认知学习中，名词化具有创造一个第一类客体的潜力，这种客体可以被直接操纵、记录、分析，并与其他客体组成更高层次的结构。 此外，学习研究人员最近开始考虑如何通过探索问题空间的内在动机来增强学习。 因此，名词化既可以作为一种直接的策略，也可以通过内在动机间接地发挥作用。 该项目最重要的广泛影响将是提供一种新的干预措施，提高单独或在协作小组中工作的问题解决者的创造力和效率。 如果成功的话，干预的相对简单性及其普遍适用性将使其成为广泛传播到不同行业的人们的主要候选人。